An Explorable Multiverse Analysis of Durante et al. (2013)
Abhraneel Sarma
2/19/2022
knit_as_emar()Introduction
Steegen and colleagues [1] introduced the concept of multiverse analysis, which they illustrated by re-analyzing data from a 2013 paper by Durante and colleagues [2] entitled “The fluctuating female vote: Politics, religion, and the ovulatory cycle”. In this paper, we reproduce a small part of Steegen et al.’s multiverse analysis of Durante et al.’s study using explorable explanations. The data processing options can be selected interactively, which allows us to show the interaction plot reported in Durante et al. in addition to the p-value.
Analysis
The default analysis below reflects the choices made by Durante et
al. [2]. Other options reflect alternatives considered by Steegen et
al. [1]. Much of the text below is copied from Steegen et al. [1], in
order to give an idea of what their article could have looked liked had
they used explorable explanations. We first begin by loading the data
used in the analysis and transforming six variables
(Abortion, StemCell…, Profit) and
creating three new variables (FiscConsComp,
SocConsComp, RelComp).
data("durante")
data.raw.study2 <- durante %>%
mutate(
Abortion = abs(7 - Abortion) + 1,
StemCell = abs(7 - StemCell) + 1,
Marijuana = abs(7 - Marijuana) + 1,
RichTax = abs(7 - RichTax) + 1,
StLiving = abs(7 - StLiving) + 1,
Profit = abs(7 - Profit) + 1,
FiscConsComp = FreeMarket + PrivSocialSec + RichTax + StLiving + Profit,
SocConsComp = Marriage + RestrictAbortion + Abortion + StemCell + Marijuana,
RelComp = round((Rel1 + Rel2 + Rel3)/3, 2)
)To implement a multiverse analysis, we first define the multiverse object:
M = multiverse()Fertility
Durante et al. classify women into a high or low fertility group
based on cycle day. There are different reasonable ways of estimating a
woman’s next menstrual onset, which is an intermediate step in
determining cycle day. The perform this calculation in two ways
df = data.raw.study2 %>%
mutate(ComputedCycleLength = StartDateofLastPeriod - StartDateofPeriodBeforeLast) %>%
mutate(NextMenstrualOnset = StartDateofLastPeriod + ComputedCycleLength) %>%
mutate(CycleDay = 28 - (NextMenstrualOnset - DateTesting), CycleDay = ifelse(WorkerID ==
15, 11, ifelse(WorkerID == 16, 18, CycleDay)), CycleDay = ifelse(CycleDay >
1 & CycleDay < 28, CycleDay, ifelse(CycleDay < 1, 1, 28)))df = data.raw.study2 %>%
mutate(ComputedCycleLength = StartDateofLastPeriod - StartDateofPeriodBeforeLast) %>%
mutate(NextMenstrualOnset = StartDateofLastPeriod + ReportedCycleLength) %>%
mutate(CycleDay = 28 - (NextMenstrualOnset - DateTesting), CycleDay = ifelse(WorkerID ==
15, 11, ifelse(WorkerID == 16, 18, CycleDay)), CycleDay = ifelse(CycleDay >
1 & CycleDay < 28, CycleDay, ifelse(CycleDay < 1, 1, 28)))df = data.raw.study2 %>%
mutate(ComputedCycleLength = StartDateofLastPeriod - StartDateofPeriodBeforeLast) %>%
mutate(NextMenstrualOnset = StartDateNext) %>%
mutate(CycleDay = 28 - (NextMenstrualOnset - DateTesting), CycleDay = ifelse(WorkerID ==
15, 11, ifelse(WorkerID == 16, 18, CycleDay)), CycleDay = ifelse(CycleDay >
1 & CycleDay < 28, CycleDay, ifelse(CycleDay < 1, 1, 28)))df = data.raw.study2 %>%
mutate( ComputedCycleLength = StartDateofLastPeriod - StartDateofPeriodBeforeLast ) %>%
mutate(NextMenstrualOnset = branch(menstrual_calculation,
"mc_option1" ~ StartDateofLastPeriod + ComputedCycleLength,
"mc_option2" ~ StartDateofLastPeriod + ReportedCycleLength,
"mc_option3" ~ StartDateNext)
) %>%
mutate(
CycleDay = 28 - (NextMenstrualOnset - DateTesting),
CycleDay = ifelse(WorkerID == 15, 11, ifelse(WorkerID == 16, 18, CycleDay)),
CycleDay = ifelse(CycleDay > 1 & CycleDay < 28, CycleDay, ifelse(CycleDay < 1, 1, 28))
)The classification of women into a high or low fertility group based
on cycle day can be done in several ways
df = df %>%
mutate(Fertility = factor(ifelse(CycleDay >= 7 & CycleDay <= 14, "high", ifelse(CycleDay >=
17 & CycleDay <= 25, "low", NA))))df = df %>%
mutate(Fertility = factor(ifelse(CycleDay >= 6 & CycleDay <= 14, "high", ifelse(CycleDay >=
17 & CycleDay <= 27, "low", NA))))df = df %>%
mutate(Fertility = factor(ifelse(CycleDay >= 9 & CycleDay <= 17, "high", ifelse(CycleDay >=
18 & CycleDay <= 25, "low", NA))))df = df %>%
mutate(Fertility = factor(ifelse(CycleDay >= 8 & CycleDay <= 14, "high", "low")))df = df %>%
mutate(Fertility = factor(ifelse(CycleDay >= 8 & CycleDay <= 17, "high", "low")))df = df %>%
mutate(Fertility = factor(ifelse(CycleDay >= 7 & CycleDay <= 14, "high", ifelse(CycleDay >=
17 & CycleDay <= 25, "low", NA))))df = df %>%
mutate(Fertility = factor(ifelse(CycleDay >= 6 & CycleDay <= 14, "high", ifelse(CycleDay >=
17 & CycleDay <= 27, "low", NA))))df = df %>%
mutate(Fertility = factor(ifelse(CycleDay >= 9 & CycleDay <= 17, "high", ifelse(CycleDay >=
18 & CycleDay <= 25, "low", NA))))df = df %>%
mutate(Fertility = factor(ifelse(CycleDay >= 8 & CycleDay <= 14, "high", "low")))df = df %>%
mutate(Fertility = factor(ifelse(CycleDay >= 8 & CycleDay <= 17, "high", "low")))df = df %>%
mutate(Fertility = factor(ifelse(CycleDay >= 7 & CycleDay <= 14, "high", ifelse(CycleDay >=
17 & CycleDay <= 25, "low", NA))))df = df %>%
mutate(Fertility = factor(ifelse(CycleDay >= 6 & CycleDay <= 14, "high", ifelse(CycleDay >=
17 & CycleDay <= 27, "low", NA))))df = df %>%
mutate(Fertility = factor(ifelse(CycleDay >= 9 & CycleDay <= 17, "high", ifelse(CycleDay >=
18 & CycleDay <= 25, "low", NA))))df = df %>%
mutate(Fertility = factor(ifelse(CycleDay >= 8 & CycleDay <= 14, "high", "low")))df = df %>%
mutate(Fertility = factor(ifelse(CycleDay >= 8 & CycleDay <= 17, "high", "low")))df = df %>%
mutate( Fertility = branch( fertile,
"fer_option1" ~ factor( ifelse(CycleDay >= 7 & CycleDay <= 14, "high", ifelse(CycleDay >= 17 & CycleDay <= 25, "low", NA)) ),
"fer_option2" ~ factor( ifelse(CycleDay >= 6 & CycleDay <= 14, "high", ifelse(CycleDay >= 17 & CycleDay <= 27, "low", NA)) ),
"fer_option3" ~ factor( ifelse(CycleDay >= 9 & CycleDay <= 17, "high", ifelse(CycleDay >= 18 & CycleDay <= 25, "low", NA)) ),
"fer_option4" ~ factor( ifelse(CycleDay >= 8 & CycleDay <= 14, "high", "low") ),
"fer_option5" ~ factor( ifelse(CycleDay >= 8 & CycleDay <= 17, "high", "low") )
))Relationship status
There are at least three options for the dichotomization of women’s
relationship status into single or committed
df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
2, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
3 | Relationship == 4, "Relationship", NA))))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
2, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
3 | Relationship == 4, "Relationship", NA))))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
2, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
3 | Relationship == 4, "Relationship", NA))))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
2, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
3 | Relationship == 4, "Relationship", NA))))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
2, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
3 | Relationship == 4, "Relationship", NA))))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
2, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
3 | Relationship == 4, "Relationship", NA))))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
2, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
3 | Relationship == 4, "Relationship", NA))))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
2, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
3 | Relationship == 4, "Relationship", NA))))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
2, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
3 | Relationship == 4, "Relationship", NA))))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
2, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
3 | Relationship == 4, "Relationship", NA))))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
2, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
3 | Relationship == 4, "Relationship", NA))))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
2, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
3 | Relationship == 4, "Relationship", NA))))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
2, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
3 | Relationship == 4, "Relationship", NA))))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
2, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
3 | Relationship == 4, "Relationship", NA))))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
2, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
3 | Relationship == 4, "Relationship", NA))))df = df %>%
mutate(RelationshipStatus = branch(relationship_status,
"rs_option1" ~ factor(ifelse(Relationship==1 | Relationship==2, 'Single', 'Relationship')),
"rs_option2" ~ factor(ifelse(Relationship==1, 'Single', 'Relationship')),
"rs_option3" ~ factor(ifelse(Relationship==1, 'Single', ifelse(Relationship==3 | Relationship==4, 'Relationship', NA))) )
)Exclusion criteria
The assignment of the participants to a high or low fertility group
automatically excludes women whose cycle days are not in the high or low
fertility range
Because a lot of this data involves participants’ self reported
measures, it is not unreasonable to exclude participants who are unsure
of their responses
Results
The authors evaluate the impact of a participants
Fertility, RelationshipStatus and it’s
interaction on their Religiosity which is defined using the
composite variable RelComp.
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm(RelComp ~ 0 + Fertility * RelationshipStatus, data = df)
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, "*")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "InRelationship",
"Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower),
.groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) + geom_bar(stat = "identity",
position = position_dodge2(preserve = "single"), width = 0.5) + geom_linerange(aes(ymin = .lower,
ymax = .upper), position = position_dodge(width = 0.5)) + geom_text(label = paste0("Interaction:",
p_val), x = 1.5, y = 7.5, colour = "#666666") + labs(x = "RelationshipStatus",
y = "ReligiosityCompositeScore") + ylim(c(0, 8.1)) + theme_minimal()
fit_RelComp <- lm( RelComp ~ 0 + Fertility * RelationshipStatus, data = df )
p_val = round(summary(fit_RelComp)$coefficients[4, 4], 3)
if (p_val < 0.05) p_val = paste0(p_val, " *")
broom::augment(fit_RelComp, interval = "confidence") %>%
group_by(Fertility, RelationshipStatus) %>%
mutate(RelationshipStatus = ifelse(RelationshipStatus == "Relationship", "In Relationship", "Single")) %>%
summarise(.fitted = mean(.fitted), .upper = mean(.upper), .lower = mean(.lower), .groups = "drop") %>%
ggplot(aes(x = RelationshipStatus, y = .fitted, fill = Fertility)) +
geom_bar(stat = "identity", position = position_dodge2(preserve = "single"), width = 0.5) +
geom_linerange(aes(ymin = .lower, ymax = .upper), position = position_dodge(width = 0.5)) +
geom_text(label = paste0("Interaction: ", p_val), x = 1.5, y = 7.5, colour = "#666666") +
labs(x = "Relationship Status", y = "Religiosity Composite Score") +
ylim(c(0, 8.1)) +
theme_minimal()The interaction between relationship status and fertility in study 1 is shown in Figure 1. This plot reproduces Figure 1 from Durante et al.’s article [2] but with the y-axis starting at zero. This figure does not appear Steegen et al.’s multiverse analysis [1], as there would be 180 such figures to show, which would be impractical with a static paper. The p-value for the interaction is also shown on Figure 1.
Discussion
As in the original paper, we can see that “the multiverse analysis revealed that almost all choice combinations for data processing lead to large p values” [1] and we can again conclude that “the effect of fertility on religion seems too sensitive to arbitrary choices and thus too fragile to be taken seriously” [1]. Figure 1 can animated by holding the ‘A’ key, giving a striking demonstration of the variability of effect sizes across the multiverse that can usefully complement Steegen et al’s histogram of p-values.